In recent years, as electric automobiles, hybrid automobiles, and the like increase and photovoltaic generation is prevailing, storage batteries for storing electric energy become increasingly important.
The storage battery is required to stably supply power to various devices in which the storage battery is incorporated, and therefore the state of the storage battery needs to be managed.
Examples of the states of the storage battery that should be managed include the temperature, the remaining stored power amount (state of charge), and the deterioration state of the storage battery. Among these, in particular, the state of charge (SOC) is a basic parameter indicating the state of the storage battery, and high accuracy is required for the estimation thereof.
Therefore, as an estimation method for the SOC of the storage battery, estimation methods such as an output voltage method, an internal resistance method, a current integration method, have been proposed conventionally. However, in order to achieve SOC estimation with higher accuracy, a method of estimating the SOC of the storage battery using a Kalman filter is sometimes employed (see, for example, NON PATENT LITERATURE 1).
The SOC estimation method using a Kalman filter has advantages that it is possible to predict the remaining amount with high accuracy, and in addition, that the remaining amount can be predicted even if the initial remaining amount of the storage battery is unknown.